*/
    public static String createMavenVersionString(Properties buildProperties) {
        String version = reduce(buildProperties.getProperty(BUILD_VERSION_PROPERTY));
        String rev = reduce(buildProperties.getProperty("buildNumber"));
        String distributionName = reduce(buildProperties.getProperty("distributionName"));

        return distributionName + " "

    public static String createMavenVersionString(Properties buildProperties) {
        String timestamp = reduce(buildProperties.getProperty("timestamp"));
        String version = reduce(buildProperties.getProperty(BUILD_VERSION_PROPERTY));
        String rev = reduce(buildProperties.getProperty("buildNumber"));
        String distributionName = reduce(buildProperties.getProperty("distributionName"));

        String msg = distributionName + " ";

/**
 * A pluggable service for processing model objects during model building.
 *
 * <p>This service allows implementations to:</p>
 * <ul>
 *   <li>Pool identical objects to reduce memory footprint</li>
 *   <li>Intern objects for faster equality comparisons</li>
 *   <li>Apply custom optimization strategies</li>
 *   <li>Transform or modify objects during building</li>
 * </ul>
 *

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

Doing this provides the following specific benefits:
- **Reduce the API surface** - We no longer need to maintain two methods.
- **Consistency** - All languages have consistent access to the same APIs and ergonomics in the DSL.
- **Reduce the size of the Gradle distribution** - We no longer need to carry multiple standard libraries for different languages.

## Decision

mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk

				// Number of probes.
				// ~ cat /proc/sys/net/ipv4/tcp_keepalive_probes (defaults to 9, we reduce it to 5)
				_ = syscall.SetsockoptInt(fd, syscall.IPPROTO_TCP, syscall.TCP_KEEPCNT, 5)

				// Wait time after successful probe in seconds.
				// ~ cat /proc/sys/net/ipv4/tcp_keepalive_intvl (defaults to 75 secs, we reduce it to 15 secs)
				_ = syscall.SetsockoptInt(fd, syscall.IPPROTO_TCP, syscall.TCP_KEEPINTVL, 15)
			}

                        .map(deps -> removeManagedDependenciesFromSection(
                                context, deps, allGAVs, "profile dependencyManagement"))
                        .orElse(false))
                .reduce(false, Boolean::logicalOr);

        hasChanges |= profileChanges;

        return hasChanges;
    }

    /**
     * Applies dependency inference redundancy optimizations.

        return dependencyContainers
                .map(container -> fixDuplicateDependenciesInSection(container.element, context, container.sectionName))
                .reduce(false, Boolean::logicalOr);
    }

    private static class DependencyContainer {
        final Element element;
        final String sectionName;

net.ipv4.tcp_max_syn_backlog=16384
net.core.wmem_max=4194304
net.core.rmem_max=4194304
net.core.rmem_default=4194304
net.core.wmem_default=4194304
net.ipv4.tcp_rmem="4096 87380 4194304"
net.ipv4.tcp_wmem="4096 65536 4194304"

# Reduce CPU utilization
net.ipv4.tcp_timestamps=0

# Increase throughput
net.ipv4.tcp_sack=1

# Low latency mode for TCP
net.ipv4.tcp_low_latency=1

# The following variable is used to tell the kernel how